---
title: "Assignment 7"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
        
---

```{r setup, include=FALSE}
library(flexdashboard)
library(ggplot2)
```

Overall Summary
=====================================  

In my analysis of insurance data, I explore different aspects like where people live, whether they smoke, their body mass index (BMI), and how much they're charged for insurance. I found that some regions have more people insured, smokers tend to pay more, and as people get older, their insurance costs might go up. The data also shows that while most people have reasonable medical charges but some face very high costs. Overall, my investigation reveals interesting patterns in who pays more for insurance and why, suggesting that both health habits and where you live can make a big difference in insurance expenses.

Data Preparation and Summary
=====================================  
    
Row 
-------------------------------------
    
### 1. Read the data file insurance.csv using the read_csv() function in tidyverse.
```{r}
library(tidyverse)
library(ggplot2)
insurance<- read.csv("~/Desktop/MTH 209 /insurance.csv")
insurance
```
   
Column
-------------------------------------
   
### 2. Get a glimpse of the data and indicate the number of observations and the number of variables in the data.
```{r}
glimpse(insurance)
```
 

Distribution Analysis
=====================================   

Column {.tabset}
-------------------------------------
   
### 3. Create a bar plot of region. Use a few sentences to summarize your finding based on the plot.
```{r}
ggplot(insurance, aes(x = region)) +
  geom_bar(fill = "steelblue", color = "black") +
  labs(title = "Number of Beneficiaries by Region", x = "Region", y = "Count") +
  theme_minimal()
```

The bar graph appears to shpw the southeast region to have a slightly higher number of beneficiaries that anywhere else. The data is fairly semetrical and evenly distributed.


### 4. Create a stack bar plot such that region is on the x axis and each bar shows the distribution of smoker in that region. You should make sure that your y axis shows percents.
```{r}
insurance %>%
  group_by(region, smoker) %>%
  summarise(count = n()) %>%
  mutate(perc = count / sum(count) * 100) %>%
  ggplot(aes(x = region, y = perc, fill = smoker)) +
  geom_bar(stat = "identity") +
  scale_y_continuous(labels = scales::percent_format()) +
  labs(title = "Distribution of Smokers by Region", x = "Region", y = "Percentage", fill = "Smoker Status") +
  theme_minimal()
```

   
### 5. Create a histogram of bmi. Discuss the distribution of the histogram.
```{r}
ggplot(insurance, aes(x = bmi)) +
  geom_histogram(aes(y = ..density..), binwidth = 1, fill = "cornflowerblue", color = "black") +
  geom_density(alpha = .2, fill = "red") +
  labs(title = "Distribution of BMI", x = "BMI", y = "Density") +
  theme_minimal()
```

The distribution appears to be fairly semestircal but maybe slightly skewed to the right. 


Charges and Relationships {data-orientation=rows}
=====================================     
    
### 6. Create a histogram of charges, Discuss the distribution of the histogram.
```{r}
ggplot(insurance, aes(x = charges)) +
  geom_histogram(aes(y = ..density..), binwidth = 10000, fill = "lightblue", color = "black") +
  geom_density(alpha = .2, fill = "orange") +
  labs(title = "Distribution of Medical Charges", x = "Medical Charges", y = "Density") +
  theme_minimal()
```

The distribution appears to be skewed to the right.

### 7. Create a boxplot that shows the distribution of bmi based on the region. Discuss what you find based on the boxplot. (Hint: you need to have x and y variables in mapping)
```{r}
ggplot(insurance, aes(x = region, y = bmi)) +
  geom_boxplot(fill = "lightgreen", color = "darkgreen") +
  labs(title = "Distribution of BMI by Region", x = "Region", y = "BMI") +
  theme_minimal()
```
The box plot shows the southeast to have a higher mean and IQR while the rest of the data appears to be fairly similar. 

### 8. Create a scatterplot that shows the relationship between age (independent variable) and charges (dependent variable). Comment on the scatterplot.
```{r}
ggplot(insurance, aes(x = age, y = charges)) +
  geom_point(aes(color = age), alpha = 0.5) +
  scale_color_gradient(low = "blue", high = "red") +
  labs(title = "Relationship Between Age and Medical Charges", x = "Age", y = "Medical Charges") +
  theme_minimal()
```
The scatter plot shows the data to be trending upward and with little correlation. 


Smoker Analysis
===================================== 

Column {.tabset}
-------------------------------------

### 9. You should find that it seems "charges" could be classified into several groups. Let's create a scatterplot that has age as the independent variable (x) and has smoker as another categorical variable (color), and the response variable is charges. Comment on the scatterplot.
```{r}
ggplot(insurance, aes(x = age, y = charges, color = smoker)) +
  geom_point(alpha = 0.6) +
  scale_color_manual(values = c("No" = "blue", "Yes" = "red")) +
  labs(title = "Age vs. Medical Charges by Smoking Status", x = "Age", y = "Medical Charges", color = "Smoker Status") +
  theme_minimal()
```


The plot has 2 distinct groups which is intresting becase that is not seen in other plots. 

### 10. Now, create two data frames by subsetting insurance data as follows. smoker <- insurance[insurance$smoker=="yes"]
```{r}
smoker <- insurance[insurance$smoker == "yes", ]
nonsmoker <- insurance[insurance$smoker == "no", ]
```


### 11. Create a scatterplot that has age as the independent variable (x) and the response variable is charges using the data frame smoker. Then add the smooth line. Comment on the plot. Does it make sense to use the smooth line to summarize the relationship between age of clients and the corresponding charges? Why?
```{r}
ggplot(smoker, aes(x = age, y = charges)) +
  geom_point(aes(color = age), alpha = 0.6) +  # Points colored by age for additional depth
  geom_smooth(method = "lm", color = "red", se = FALSE) +  # Add a linear regression line without confidence interval
  scale_color_gradient(low = "lightblue", high = "darkblue") +
  labs(title = "Age vs. Medical Charges for Smokers", x = "Age", y = "Medical Charges") +
  theme_minimal()
```


The plot seems to be even and symmetrical. It does make sense to the use smooth line becase the data correlates to each other. 


Non-Smoker Analysis
===================================== 

### 12. Repeat Question 11 using the data frame nonsmoker.
```{r}
ggplot(nonsmoker, aes(x = age, y = charges)) +
  geom_point(aes(color = age), alpha = 0.6) +  # Points colored by age for additional depth
  geom_smooth(method = "lm", color = "green", se = FALSE) +  # Add a linear regression line without confidence interval
  scale_color_gradient(low = "lightgreen", high = "darkgreen") +
  labs(title = "Age vs. Medical Charges for Non-Smokers", x = "Age", y = "Medical Charges") +
  theme_minimal()
```


Further Exploration and Visualization
===================================== 

Column {.tabset}
-------------------------------------

### 13. Based on the finding you have on Questions 11 & 12, propose what you might do next if you want to model charges using other variables in this data.

To better predict medical costs, include more details like BMI, gender, kids, and where people live, and try different models to see what works best.

### 14. Create a pie chart of children. Use a few sentences to summarize your finding based on the plot. (Hint: You need to convert the variable to a categorical variable first)
```{r}
insurance$children <- as.factor(insurance$children)
ggplot(insurance) +
  geom_bar(aes(x = "", fill = children), width = 1) +
  coord_polar(theta = "y") +
  labs(title = "Distribution of Number of Children Covered", x = "", y = "", fill = "Number of Children") +
  theme_void()
```

### 15. Create a boxplot that shows the distribution of charges based on the number of children. Discuss what you find based on the boxplot.
```{r}
insurance$children <- as.factor(insurance$children)
ggplot(insurance, aes(x = children, y = charges)) +
  geom_boxplot(fill = "lightblue") +
  labs(title = "Distribution of Medical Charges by Number of Children", x = "Number of Children", y = "Medical Charges") +
  theme_minimal()
```
The boxplot shows all the data to have a lot of outlyers and the means are all similar.